The olfactory system employs an ensemble of odorant receptors (ORs) to sense odorants and to derive olfactory percepts. We trained artificial neural networks to represent the chemical space of odorants and used that representation to predict human olfactory percepts. We hypothesized that ORs may be considered 3D convolutional filters that extract molecular features and can be trained using machine learning methods. First, we trained a convolutional autoencoder, called DeepNose, to deduce a low-dimensional representation of odorant molecules which were represented by their 3D spatial structure. Next, we tested the ability of DeepNose features in predicting physical properties and odorant percepts based on 3D molecular structure alone. We found that despite the lack of human expertise, DeepNose features led to predictions of both physical properties and perceptions of comparable accuracy to molecular descriptors often used in computational chemistry, such as Dragon descriptors. We propose that DeepNose network can extract de novo chemical features predictive of various bioactivities and can help understand the factors influencing the composition of ORs ensemble.
Ngoc "Tumi" Tran (Cold Spring Harbor Laboratory)
Daniel Kepple (Cold Spring Harbor Laboratory)
Sergey Shuvaev (Cold Spring Harbor Laboratory)
Alexei Koulakov (Cold Spring Harbor Laboratory)
Related Events (a corresponding poster, oral, or spotlight)
2019 Poster: DeepNose: Using artificial neural networks to represent the space of odorants »
Wed Jun 12th 06:30 -- 09:00 PM Room Pacific Ballroom